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Bug fix regarding bias
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a0b2276f68
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@ -432,8 +432,8 @@ class MaxEigLimiterFunction(torch.autograd.Function):
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class BasicNormFunction(torch.autograd.Function):
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# This computes:
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# scales = torch.mean((x + bias) ** 2, keepdim=True) + eps.exp()
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# return x * scales
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# scales = torch.mean((x - bias) ** 2, keepdim=True) + eps.exp()
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# return (x - bias) * scales
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# (after unsqueezing the bias), but it does it in a memory-efficient way so that
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# it can just store the returned value (chances are, this will also be needed for
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# some other reason, related to the next operation, so we can save memory).
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@ -448,8 +448,8 @@ class BasicNormFunction(torch.autograd.Function):
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ctx.channel_dim = channel_dim
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for _ in range(channel_dim + 1, x.ndim):
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bias = bias.unsqueeze(-1)
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scales = (torch.mean((x + bias) ** 2, dim=channel_dim, keepdim=True) + eps.exp()) ** -0.5
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ans = x * scales - bias
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scales = (torch.mean((x - bias) ** 2, dim=channel_dim, keepdim=True) + eps.exp()) ** -0.5
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ans = x * scales
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ctx.save_for_backward(ans if store_output_for_backprop else x,
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scales, bias, eps)
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return ans
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@ -470,8 +470,8 @@ class BasicNormFunction(torch.autograd.Function):
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eps.requires_grad = True
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with torch.enable_grad():
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# recompute scales from x, bias and eps.
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scales = (torch.mean((x + bias) ** 2, dim=ctx.channel_dim, keepdim=True) + eps.exp()) ** -0.5
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ans = x * scales - bias
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scales = (torch.mean((x - bias) ** 2, dim=ctx.channel_dim, keepdim=True) + eps.exp()) ** -0.5
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ans = x * scales
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ans.backward(gradient=ans_grad)
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return x.grad, bias.grad.flatten(), eps.grad, None, None
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